1. Field of the Invention
The present invention relates to a fuzzy inference (or approximate inference or reasoning) system.
2. Description of Related Art
Conventional fuzzy inference systems have so far not been able to switch inference rule groups at high speeds when inferences are being executed. Therefore, a high-speed inference system has executed the inference processing in accordance with a rule group corresponding to a single task.
In spite of the fact that an ordinary binary-type computer can execute so-called multitask processing, no inference system which can execute multitask processing has been so far realized. Since many objects to be controlled do not necessarily require high speed inference processing, a high speed inference system, cannot use all of its capabilities.
On the other hand, pipeline processing methods have been adopted for conventional binary computers as a method of realizing high speed operation, through utilizing a special architecture. However, since control codes for controlling pipeline processing and the object data to be processed are frequently supplied to the pipeline processing system at different timings, a waiting time often results in the processing means based on the delay between the pipeline control code and the object data. In addition, since the control codes and object data to be processed are supplied externally, these codes and data may not be supplied at the appropriate timings for the process. Further, the waiting time often occurs in the pipeline system when the time at which the processed result is outputted, is delayed. The above-mentioned problems may thereby arise when an inference system is used in a pipeline architecture or format.
In fuzzy inference systems, a number of membership functions are determined in accordance with an inference rule group. Therefore, when input values are given, function values of the membership functions corresponding to input values (the degree to which the input values belong to the membership functions and referred to as adaptability or truth values) can be obtained. In conventional fuzzy inference systems, however, since the membership functions are fixedly set to the hardware construction or the function values of the membership functions are stored in a memory unit, it is difficult to alter, without undue time and labor, the fixedly determined membership functions or to improve the tuning flexibility in the membership functions. Further, when various membership functions required for inference processing are stored in a memory unit, the sheer amount of data in the memory unit will become increasingly large.
When the inference system is adapted to a pipeline control architecture, it becomes extremely important to supply data suitable for that architecture. It also is necessary to construct the system in such a way that the reduction in waiting time can be optimized to increase throughput.
Methods of transforming arithmetic operation programs executed by a microcomputer into data suitable for pipeline control processing are well known. An example of such a method is disclosed in Japanese Patent Application Kokai Publication No. 64-66734. However, this method cannot transform rule groups that are usable for fuzzy inference processing into a data form that is suitable for a pipeline architecture. This is because, in contrast to microcomputer instructions, fuzzy rules are different from each other in the number and sort of input variables for the antecedent and therefore are not suitable for the pipeline architecture which is based on uniform instructions. As a result, a problem exists in applying and adapting fuzzy inference processing to a pipeline architecture in order to improve the inference speed.